Final Project DSC 540

Vismay Patel

EDA ANALYSIS AND IMPLEMENTATION OF DIFFERENT TECHNIQUES ON BANKRUPTCY DATASET

# IMPORTING essential libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Loading the dataset as a csv file

bankrupt = pd.read_csv("D:/DEPAUL/DePaul 6th quaeter (Winter 2021 JAN)/DSC 540/PROJECT/data.csv")
bankrupt.head()

	Bankrupt?	ROA(C) before interest and depreciation before interest	ROA(A) before interest and % after tax	ROA(B) before interest and depreciation after tax	Operating Gross Margin	Realized Sales Gross Margin	Operating Profit Rate	Pre-tax net Interest Rate	After-tax net Interest Rate	Non-industry income and expenditure/revenue	...	Net Income to Total Assets	Total assets to GNP price	No-credit Interval	Gross Profit to Sales	Net Income to Stockholder's Equity	Liability to Equity	Degree of Financial Leverage (DFL)	Interest Coverage Ratio (Interest expense to EBIT)	Net Income Flag	Equity to Liability
0	1	0.370594	0.424389	0.405750	0.601457	0.601457	0.998969	0.796887	0.808809	0.302646	...	0.716845	0.009219	0.622879	0.601453	0.827890	0.290202	0.026601	0.564050	1	0.016469
1	1	0.464291	0.538214	0.516730	0.610235	0.610235	0.998946	0.797380	0.809301	0.303556	...	0.795297	0.008323	0.623652	0.610237	0.839969	0.283846	0.264577	0.570175	1	0.020794
2	1	0.426071	0.499019	0.472295	0.601450	0.601364	0.998857	0.796403	0.808388	0.302035	...	0.774670	0.040003	0.623841	0.601449	0.836774	0.290189	0.026555	0.563706	1	0.016474
3	1	0.399844	0.451265	0.457733	0.583541	0.583541	0.998700	0.796967	0.808966	0.303350	...	0.739555	0.003252	0.622929	0.583538	0.834697	0.281721	0.026697	0.564663	1	0.023982
4	1	0.465022	0.538432	0.522298	0.598783	0.598783	0.998973	0.797366	0.809304	0.303475	...	0.795016	0.003878	0.623521	0.598782	0.839973	0.278514	0.024752	0.575617	1	0.035490

5 rows × 96 columns

bankrupt.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6819 entries, 0 to 6818
Data columns (total 96 columns):
 #   Column                                                    Non-Null Count  Dtype  
---  ------                                                    --------------  -----  
 0   Bankrupt?                                                 6819 non-null   int64  
 1    ROA(C) before interest and depreciation before interest  6819 non-null   float64
 2    ROA(A) before interest and % after tax                   6819 non-null   float64
 3    ROA(B) before interest and depreciation after tax        6819 non-null   float64
 4    Operating Gross Margin                                   6819 non-null   float64
 5    Realized Sales Gross Margin                              6819 non-null   float64
 6    Operating Profit Rate                                    6819 non-null   float64
 7    Pre-tax net Interest Rate                                6819 non-null   float64
 8    After-tax net Interest Rate                              6819 non-null   float64
 9    Non-industry income and expenditure/revenue              6819 non-null   float64
 10   Continuous interest rate (after tax)                     6819 non-null   float64
 11   Operating Expense Rate                                   6819 non-null   float64
 12   Research and development expense rate                    6819 non-null   float64
 13   Cash flow rate                                           6819 non-null   float64
 14   Interest-bearing debt interest rate                      6819 non-null   float64
 15   Tax rate (A)                                             6819 non-null   float64
 16   Net Value Per Share (B)                                  6819 non-null   float64
 17   Net Value Per Share (A)                                  6819 non-null   float64
 18   Net Value Per Share (C)                                  6819 non-null   float64
 19   Persistent EPS in the Last Four Seasons                  6819 non-null   float64
 20   Cash Flow Per Share                                      6819 non-null   float64
 21   Revenue Per Share (Yuan ¥)                               6819 non-null   float64
 22   Operating Profit Per Share (Yuan ¥)                      6819 non-null   float64
 23   Per Share Net profit before tax (Yuan ¥)                 6819 non-null   float64
 24   Realized Sales Gross Profit Growth Rate                  6819 non-null   float64
 25   Operating Profit Growth Rate                             6819 non-null   float64
 26   After-tax Net Profit Growth Rate                         6819 non-null   float64
 27   Regular Net Profit Growth Rate                           6819 non-null   float64
 28   Continuous Net Profit Growth Rate                        6819 non-null   float64
 29   Total Asset Growth Rate                                  6819 non-null   float64
 30   Net Value Growth Rate                                    6819 non-null   float64
 31   Total Asset Return Growth Rate Ratio                     6819 non-null   float64
 32   Cash Reinvestment %                                      6819 non-null   float64
 33   Current Ratio                                            6819 non-null   float64
 34   Quick Ratio                                              6819 non-null   float64
 35   Interest Expense Ratio                                   6819 non-null   float64
 36   Total debt/Total net worth                               6819 non-null   float64
 37   Debt ratio %                                             6819 non-null   float64
 38   Net worth/Assets                                         6819 non-null   float64
 39   Long-term fund suitability ratio (A)                     6819 non-null   float64
 40   Borrowing dependency                                     6819 non-null   float64
 41   Contingent liabilities/Net worth                         6819 non-null   float64
 42   Operating profit/Paid-in capital                         6819 non-null   float64
 43   Net profit before tax/Paid-in capital                    6819 non-null   float64
 44   Inventory and accounts receivable/Net value              6819 non-null   float64
 45   Total Asset Turnover                                     6819 non-null   float64
 46   Accounts Receivable Turnover                             6819 non-null   float64
 47   Average Collection Days                                  6819 non-null   float64
 48   Inventory Turnover Rate (times)                          6819 non-null   float64
 49   Fixed Assets Turnover Frequency                          6819 non-null   float64
 50   Net Worth Turnover Rate (times)                          6819 non-null   float64
 51   Revenue per person                                       6819 non-null   float64
 52   Operating profit per person                              6819 non-null   float64
 53   Allocation rate per person                               6819 non-null   float64
 54   Working Capital to Total Assets                          6819 non-null   float64
 55   Quick Assets/Total Assets                                6819 non-null   float64
 56   Current Assets/Total Assets                              6819 non-null   float64
 57   Cash/Total Assets                                        6819 non-null   float64
 58   Quick Assets/Current Liability                           6819 non-null   float64
 59   Cash/Current Liability                                   6819 non-null   float64
 60   Current Liability to Assets                              6819 non-null   float64
 61   Operating Funds to Liability                             6819 non-null   float64
 62   Inventory/Working Capital                                6819 non-null   float64
 63   Inventory/Current Liability                              6819 non-null   float64
 64   Current Liabilities/Liability                            6819 non-null   float64
 65   Working Capital/Equity                                   6819 non-null   float64
 66   Current Liabilities/Equity                               6819 non-null   float64
 67   Long-term Liability to Current Assets                    6819 non-null   float64
 68   Retained Earnings to Total Assets                        6819 non-null   float64
 69   Total income/Total expense                               6819 non-null   float64
 70   Total expense/Assets                                     6819 non-null   float64
 71   Current Asset Turnover Rate                              6819 non-null   float64
 72   Quick Asset Turnover Rate                                6819 non-null   float64
 73   Working capitcal Turnover Rate                           6819 non-null   float64
 74   Cash Turnover Rate                                       6819 non-null   float64
 75   Cash Flow to Sales                                       6819 non-null   float64
 76   Fixed Assets to Assets                                   6819 non-null   float64
 77   Current Liability to Liability                           6819 non-null   float64
 78   Current Liability to Equity                              6819 non-null   float64
 79   Equity to Long-term Liability                            6819 non-null   float64
 80   Cash Flow to Total Assets                                6819 non-null   float64
 81   Cash Flow to Liability                                   6819 non-null   float64
 82   CFO to Assets                                            6819 non-null   float64
 83   Cash Flow to Equity                                      6819 non-null   float64
 84   Current Liability to Current Assets                      6819 non-null   float64
 85   Liability-Assets Flag                                    6819 non-null   int64  
 86   Net Income to Total Assets                               6819 non-null   float64
 87   Total assets to GNP price                                6819 non-null   float64
 88   No-credit Interval                                       6819 non-null   float64
 89   Gross Profit to Sales                                    6819 non-null   float64
 90   Net Income to Stockholder's Equity                       6819 non-null   float64
 91   Liability to Equity                                      6819 non-null   float64
 92   Degree of Financial Leverage (DFL)                       6819 non-null   float64
 93   Interest Coverage Ratio (Interest expense to EBIT)       6819 non-null   float64
 94   Net Income Flag                                          6819 non-null   int64  
 95   Equity to Liability                                      6819 non-null   float64
dtypes: float64(93), int64(3)
memory usage: 5.0 MB

No missing values in the dataset

bankrupt.describe()

	Bankrupt?	ROA(C) before interest and depreciation before interest	ROA(A) before interest and % after tax	ROA(B) before interest and depreciation after tax	Operating Gross Margin	Realized Sales Gross Margin	Operating Profit Rate	Pre-tax net Interest Rate	After-tax net Interest Rate	Non-industry income and expenditure/revenue	...	Net Income to Total Assets	Total assets to GNP price	No-credit Interval	Gross Profit to Sales	Net Income to Stockholder's Equity	Liability to Equity	Degree of Financial Leverage (DFL)	Interest Coverage Ratio (Interest expense to EBIT)	Net Income Flag	Equity to Liability
count	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	...	6819.000000	6.819000e+03	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.000000	6819.0	6819.000000
mean	0.032263	0.505180	0.558625	0.553589	0.607948	0.607929	0.998755	0.797190	0.809084	0.303623	...	0.807760	1.862942e+07	0.623915	0.607946	0.840402	0.280365	0.027541	0.565358	1.0	0.047578
std	0.176710	0.060686	0.065620	0.061595	0.016934	0.016916	0.013010	0.012869	0.013601	0.011163	...	0.040332	3.764501e+08	0.012290	0.016934	0.014523	0.014463	0.015668	0.013214	0.0	0.050014
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.0	0.000000
25%	0.000000	0.476527	0.535543	0.527277	0.600445	0.600434	0.998969	0.797386	0.809312	0.303466	...	0.796750	9.036205e-04	0.623636	0.600443	0.840115	0.276944	0.026791	0.565158	1.0	0.024477
50%	0.000000	0.502706	0.559802	0.552278	0.605997	0.605976	0.999022	0.797464	0.809375	0.303525	...	0.810619	2.085213e-03	0.623879	0.605998	0.841179	0.278778	0.026808	0.565252	1.0	0.033798
75%	0.000000	0.535563	0.589157	0.584105	0.613914	0.613842	0.999095	0.797579	0.809469	0.303585	...	0.826455	5.269777e-03	0.624168	0.613913	0.842357	0.281449	0.026913	0.565725	1.0	0.052838
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	9.820000e+09	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.0	1.000000

8 rows × 96 columns

---

Changing column names

bankrupt.columns

Index(['Bankrupt?', ' ROA(C) before interest and depreciation before interest',
       ' ROA(A) before interest and % after tax',
       ' ROA(B) before interest and depreciation after tax',
       ' Operating Gross Margin', ' Realized Sales Gross Margin',
       ' Operating Profit Rate', ' Pre-tax net Interest Rate',
       ' After-tax net Interest Rate',
       ' Non-industry income and expenditure/revenue',
       ' Continuous interest rate (after tax)', ' Operating Expense Rate',
       ' Research and development expense rate', ' Cash flow rate',
       ' Interest-bearing debt interest rate', ' Tax rate (A)',
       ' Net Value Per Share (B)', ' Net Value Per Share (A)',
       ' Net Value Per Share (C)', ' Persistent EPS in the Last Four Seasons',
       ' Cash Flow Per Share', ' Revenue Per Share (Yuan ¥)',
       ' Operating Profit Per Share (Yuan ¥)',
       ' Per Share Net profit before tax (Yuan ¥)',
       ' Realized Sales Gross Profit Growth Rate',
       ' Operating Profit Growth Rate', ' After-tax Net Profit Growth Rate',
       ' Regular Net Profit Growth Rate', ' Continuous Net Profit Growth Rate',
       ' Total Asset Growth Rate', ' Net Value Growth Rate',
       ' Total Asset Return Growth Rate Ratio', ' Cash Reinvestment %',
       ' Current Ratio', ' Quick Ratio', ' Interest Expense Ratio',
       ' Total debt/Total net worth', ' Debt ratio %', ' Net worth/Assets',
       ' Long-term fund suitability ratio (A)', ' Borrowing dependency',
       ' Contingent liabilities/Net worth',
       ' Operating profit/Paid-in capital',
       ' Net profit before tax/Paid-in capital',
       ' Inventory and accounts receivable/Net value', ' Total Asset Turnover',
       ' Accounts Receivable Turnover', ' Average Collection Days',
       ' Inventory Turnover Rate (times)', ' Fixed Assets Turnover Frequency',
       ' Net Worth Turnover Rate (times)', ' Revenue per person',
       ' Operating profit per person', ' Allocation rate per person',
       ' Working Capital to Total Assets', ' Quick Assets/Total Assets',
       ' Current Assets/Total Assets', ' Cash/Total Assets',
       ' Quick Assets/Current Liability', ' Cash/Current Liability',
       ' Current Liability to Assets', ' Operating Funds to Liability',
       ' Inventory/Working Capital', ' Inventory/Current Liability',
       ' Current Liabilities/Liability', ' Working Capital/Equity',
       ' Current Liabilities/Equity', ' Long-term Liability to Current Assets',
       ' Retained Earnings to Total Assets', ' Total income/Total expense',
       ' Total expense/Assets', ' Current Asset Turnover Rate',
       ' Quick Asset Turnover Rate', ' Working capitcal Turnover Rate',
       ' Cash Turnover Rate', ' Cash Flow to Sales', ' Fixed Assets to Assets',
       ' Current Liability to Liability', ' Current Liability to Equity',
       ' Equity to Long-term Liability', ' Cash Flow to Total Assets',
       ' Cash Flow to Liability', ' CFO to Assets', ' Cash Flow to Equity',
       ' Current Liability to Current Assets', ' Liability-Assets Flag',
       ' Net Income to Total Assets', ' Total assets to GNP price',
       ' No-credit Interval', ' Gross Profit to Sales',
       ' Net Income to Stockholder's Equity', ' Liability to Equity',
       ' Degree of Financial Leverage (DFL)',
       ' Interest Coverage Ratio (Interest expense to EBIT)',
       ' Net Income Flag', ' Equity to Liability'],
      dtype='object')

Below are the new columns that will be assigned to the dataset

bankrupt.columns = [
                    'Bankrupt',
                    'Return_on_total_assetC', 
                    'Return_on_total_assetA',
                    'Return_on_total_assetB', 
                    'GrossProfit/NetSales',
                    'Realized_GrossProfit/NetSales',
                    'Operating_Income/NetSales',
                    'Pre-TaxIncome/NetSales',
                    'NetIncome/NetSales',
                    'Net_Non-operating_Income_Ratio',
                    'Net_Income-Exclude_DisposalGain/Loss/Net_Sales',
                    'Operating_Expenses/Net Sales',
                    'Research_and_DevelopmentExpenses/Net Sales',
                    'CashFlow_from_Operating/Current Liabilities',
                    'Interest-bearing Debt/Equity', 
                    'Effective Tax Rate',
                    'Net_Val_Per_ShareB', 
                    'Net_Val_per_ShareA', 
                    'Net_Val_Per_ShareC',
                    'EPS-Net_Income',
                    'Cash_Flow_Per_Share',
                    'Revenue_Per_Share(Yuan¥)',
                    'Operating_Income_Per_Share(Yuan¥)',
                    'Pretax_Income_Per_Share(Yuan¥)',
                    'Realized_Sales_Gross_Profit_Growth_Rate',
                    'Operating_Income_Growth',
                    'After_Tax_Net_Income_Growth',
                    'Regular_Net_Profit_Growth_Rate',
                    'Continuous_Net_Profit_Growth_Rate', 
                    'Total_Asset_Growth',
                    'Total_Equity_Growth',
                    'Return_on_Total_Asset_Growth', 
                    'Cash_Reinvestment_Ratio', 
                    'Current_Ratio',
                    'Quick_Ratio',
                    'Interest_Expense_Ratio', 
                    'Total_Liability/Equity_Ratio', 
                    'Debt_ratio%',
                    'Networth/Assets',
                    'Long-term_fund_suitability_ratio(A)',
                    'Borrowing_dependency', 
                    'Contingent_Liability/Equity',
                    'Operating_Income/Capital', 
                    'Pretax_Income/Capital',
                    '(Inventory+Accounts_Receivables)/Equity',
                    'Total_Asset_Turnover',
                    'Accounts_Receivable_Turnover',
                    'Average_Collection_Days',
                    'Inventory_Turnover_Rate(times)',
                    'Fixed_Assets_Turnover_Frequency',
                    'Equity_Turnover',
                    'Revenue_per_person',
                    'Operating_profit_per_person',
                    'FixedAssets_Per_Employee',
                    'Working_Capital_to_Total_Assets',
                    'Quick_Assets/Total_Assets',
                    'Current_Assets/Total_Assets', 
                    'Cash/Total_Assets',
                    'Quick_Assets/Current_Liability',
                    'Cash/Current_Liability',
                    'Current_Liability_to_Assets', 
                    'Operating_Funds_to_Liability', 
                    'Inventory/Working_Capital', 
                    'Inventory/Current_Liability',
                    'Current_Liabilities/Liability', 
                    'Working_Capital/Equity', 
                    'Current_Liabilities/Equity',
                    'Long-term_Liability_to_CurrentAssets',
                    'Retained_Earnings_to_Total_Assets',
                    'Total_income/Total_expense',
                    'Total_expense/Assets',
                    'CurrentAsset_TurnoverRate',
                    'QuickAsset_TurnoverRate',
                    'Workingcapitcal_TurnoverRate',
                    'Cash_Turnover_Rate', 
                    'Cash_Flow_to_Sales',
                    'Fixed_Assets_to_Assets',
                    'Current_Liability_to_Liability',
                    'Current_Liability_to_Equity', 
                    'Equity_to_Long-term_Liability',
                    'Cash_Flow_to_Total_Assets',
                    'Cash_Flow_to_Liability', 
                    'CFO_to_Assets', 
                    'Cash_Flow_to_Equity',
                    'Current_Liability_to_current_Asset',
                    'Liability-Assets_Flag',
                    'Net_Income_to_Total_asset',
                    'Total_assets_to_GNP_prine',
                    'No-credit_Interval',
                    'Gross_Profit_to_Sales',
                    'Net_Income_to_Stockholders_Equity', 
                    'Liability_to_equity',
                    'Degree_of_Financial_Leverage',
                    'Interest_Coverage_Ratio',
                    'Net_Income_Flag',
                    'Equity_to_Liability']

bankrupt.head()

	Bankrupt	Return_on_total_assetC	Return_on_total_assetA	Return_on_total_assetB	GrossProfit/NetSales	Realized_GrossProfit/NetSales	Operating_Income/NetSales	Pre-TaxIncome/NetSales	NetIncome/NetSales	Net_Non-operating_Income_Ratio	...	Net_Income_to_Total_asset	Total_assets_to_GNP_prine	No-credit_Interval	Gross_Profit_to_Sales	Net_Income_to_Stockholders_Equity	Liability_to_equity	Degree_of_Financial_Leverage	Interest_Coverage_Ratio	Net_Income_Flag	Equity_to_Liability
0	1	0.370594	0.424389	0.405750	0.601457	0.601457	0.998969	0.796887	0.808809	0.302646	...	0.716845	0.009219	0.622879	0.601453	0.827890	0.290202	0.026601	0.564050	1	0.016469
1	1	0.464291	0.538214	0.516730	0.610235	0.610235	0.998946	0.797380	0.809301	0.303556	...	0.795297	0.008323	0.623652	0.610237	0.839969	0.283846	0.264577	0.570175	1	0.020794
2	1	0.426071	0.499019	0.472295	0.601450	0.601364	0.998857	0.796403	0.808388	0.302035	...	0.774670	0.040003	0.623841	0.601449	0.836774	0.290189	0.026555	0.563706	1	0.016474
3	1	0.399844	0.451265	0.457733	0.583541	0.583541	0.998700	0.796967	0.808966	0.303350	...	0.739555	0.003252	0.622929	0.583538	0.834697	0.281721	0.026697	0.564663	1	0.023982
4	1	0.465022	0.538432	0.522298	0.598783	0.598783	0.998973	0.797366	0.809304	0.303475	...	0.795016	0.003878	0.623521	0.598782	0.839973	0.278514	0.024752	0.575617	1	0.035490

5 rows × 96 columns

Next steps:

Check if data is balanced or not.

Correlation between the variables.

Visualization to understand the nature of variables with the y attribute and relation with each other.

Parameter selection: PCA and chi-square method.

Modeling a decision tree classifier.

Modeling a SVM machine with linear and rdf kernels.

bankrupt['Bankrupt'].value_counts().plot(kind='bar', figsize = (10,10))

<AxesSubplot:>

From the above plot we can see that the data is imbalanced, and we will move forward with Exploratory analysis to get more insight on the same.

Correlation plot using heat map

%%time
corr_plot = bankrupt.corr()
plt.figure(figsize = (40,20))
sns.heatmap(corr_plot, annot = True)
plt.title("Heatmap for correlation between attributes")

Wall time: 4.82 s

Text(0.5, 1.0, 'Heatmap for correlation between attributes')

---

Generating histograms

Columns 2-11 histogram (**Note that column 1 is Y/Dependant variable (Bankrupt))

# Generating histograms by  selecting a set of 10 columns so that it is easy to
# visualize the column values.
# we will input the columns in a list and run a for loop to generate the 
# histograms, and similar will be done will all the other variables.

viz1 = ['Return_on_total_assetC', 
                    'Return_on_total_assetA',
                    'Return_on_total_assetB', 
                    'GrossProfit/NetSales',
                    'Realized_GrossProfit/NetSales',
                    'Operating_Income/NetSales',
                    'Pre-TaxIncome/NetSales',
                    'NetIncome/NetSales',
                    'Net_Non-operating_Income_Ratio',
                    'Net_Income-Exclude_DisposalGain/Loss/Net_Sales']

for i in viz1:
    plt.figure()
    sns.histplot(x = i, data=bankrupt, bins = 70)

---

Columns 2-11 distribution plot (**Note that column 1 is Y/Dependant variable (Bankrupt))

viz1 = ['Return_on_total_assetC', 
                    'Return_on_total_assetA',
                    'Return_on_total_assetB', 
                    'GrossProfit/NetSales',
                    'Realized_GrossProfit/NetSales',
                    'Operating_Income/NetSales',
                    'Pre-TaxIncome/NetSales',
                    'NetIncome/NetSales',
                    'Net_Non-operating_Income_Ratio',
                    'Net_Income-Exclude_DisposalGain/Loss/Net_Sales']

for i in viz1:
    plt.figure()
    sns.displot(x = i, data = bankrupt, kind = "ecdf", hue = "Bankrupt")

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

<Figure size 432x288 with 0 Axes>

---

Column 2-11 pariplot

# 0 here is depencant variable bankrupt.

data1 = bankrupt.iloc[:, 0:11]

sns.pairplot(data1, hue = 'Bankrupt')

<seaborn.axisgrid.PairGrid at 0x2601de09a00>

Columns 2-11 correlation plot using heatmap

# Below is the heat map from columns 0-11 where Bankrupt is dependant variable

relation = bankrupt.iloc[:,0:11]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Columns 12-21 histogram

viz2 = ['Operating_Expenses/Net Sales',
                    'Research_and_DevelopmentExpenses/Net Sales',
                    'CashFlow_from_Operating/Current Liabilities',
                    'Interest-bearing Debt/Equity', 
                    'Effective Tax Rate',
                    'Net_Val_Per_ShareB', 
                    'Net_Val_per_ShareA', 
                    'Net_Val_Per_ShareC',
                    'EPS-Net_Income',
                    'Cash_Flow_Per_Share']
for j in viz2:
    plt.figure()
    sns.histplot(x = j, data=bankrupt, bins = 70)

---

Columns 12-21 Distribution plot with KDE curve

viz2 = ['Operating_Expenses/Net Sales',
                    'Research_and_DevelopmentExpenses/Net Sales',
                    'CashFlow_from_Operating/Current Liabilities',
                    'Interest-bearing Debt/Equity', 
                    'Effective Tax Rate',
                    'Net_Val_Per_ShareB', 
                    'Net_Val_per_ShareA', 
                    'Net_Val_Per_ShareC',
                    'EPS-Net_Income',
                    'Cash_Flow_Per_Share']
for j in viz2:
    plt.figure()
    sns.displot(x = j, data = bankrupt, kind = "kde", hue = "Bankrupt")

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---

---

Column 12-21 Correlation plot using heat map

relation = bankrupt.iloc[:,11:21]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 22-31 histogram

viz3 = ['Revenue_Per_Share(Yuan¥)',
                    'Operating_Income_Per_Share(Yuan¥)',
                    'Pretax_Income_Per_Share(Yuan¥)',
                    'Realized_Sales_Gross_Profit_Growth_Rate',
                    'Operating_Income_Growth',
                    'After_Tax_Net_Income_Growth',
                    'Regular_Net_Profit_Growth_Rate',
                    'Continuous_Net_Profit_Growth_Rate', 
                    'Total_Asset_Growth',
                    'Total_Equity_Growth']
for k in viz3:
    plt.figure()
    sns.histplot(x = k, data=bankrupt, bins = 70)

---

Column 22-31 Distribution plot

viz3 = ['Revenue_Per_Share(Yuan¥)',
                    'Operating_Income_Per_Share(Yuan¥)',
                    'Pretax_Income_Per_Share(Yuan¥)',
                    'Realized_Sales_Gross_Profit_Growth_Rate',
                    'Operating_Income_Growth',
                    'After_Tax_Net_Income_Growth',
                    'Regular_Net_Profit_Growth_Rate',
                    'Continuous_Net_Profit_Growth_Rate', 
                    'Total_Asset_Growth',
                    'Total_Equity_Growth']
for l in viz3:
    plt.figure()
    sns.displot(x = l, data = bankrupt, kind = "ecdf", hue = "Bankrupt")

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Column 21-31 Correlation matrix using heat map

relation = bankrupt.iloc[:,21:31]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Columns 32-41 histogram

viz4 = ['Return_on_Total_Asset_Growth', 
                    'Cash_Reinvestment_Ratio', 
                    'Current_Ratio',
                    'Quick_Ratio',
                    'Interest_Expense_Ratio', 
                    'Total_Liability/Equity_Ratio', 
                    'Debt_ratio%',
                    'Networth/Assets',
                    'Long-term_fund_suitability_ratio(A)',
                    'Borrowing_dependency']
for m in viz4:
    plt.figure()
    sns.histplot(x = m, data=bankrupt, bins = 70)

---

Column 32-41 distribution plot

viz4 = ['Return_on_Total_Asset_Growth', 
                    'Cash_Reinvestment_Ratio', 
                    'Current_Ratio',
                    'Quick_Ratio',
                    'Interest_Expense_Ratio', 
                    'Total_Liability/Equity_Ratio', 
                    'Debt_ratio%',
                    'Networth/Assets',
                    'Long-term_fund_suitability_ratio(A)',
                    'Borrowing_dependency']
for m in viz4:
    plt.figure()
    sns.displot(x = m, data = bankrupt, kind = "ecdf", hue = "Bankrupt")

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---

Columns 32-41 Correlation plot using heatmap

relation = bankrupt.iloc[:,31:41]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 42-51 histogram

viz5 = ['Contingent_Liability/Equity',
                    'Operating_Income/Capital', 
                    'Pretax_Income/Capital',
                    '(Inventory+Accounts_Receivables)/Equity',
                    'Total_Asset_Turnover',
                    'Accounts_Receivable_Turnover',
                    'Average_Collection_Days',
                    'Inventory_Turnover_Rate(times)',
                    'Fixed_Assets_Turnover_Frequency',
                    'Equity_Turnover']
for n in viz5:
    plt.figure()
    sns.histplot(x = n, data=bankrupt, bins = 70)

---

Column 42-51 distribution plot

viz5 = ['Contingent_Liability/Equity',
                    'Operating_Income/Capital', 
                    'Pretax_Income/Capital',
                    '(Inventory+Accounts_Receivables)/Equity',
                    'Total_Asset_Turnover',
                    'Accounts_Receivable_Turnover',
                    'Average_Collection_Days',
                    'Inventory_Turnover_Rate(times)',
                    'Fixed_Assets_Turnover_Frequency',
                    'Equity_Turnover']
for n in viz5:
    plt.figure()
    sns.displot(x = n, data = bankrupt, kind = "ecdf", hue = "Bankrupt")

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---

Column 42-51 Correlation plot using heat map

relation = bankrupt.iloc[:,41:51]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 52-61 histogram

viz6 = ['Revenue_per_person',
                    'Operating_profit_per_person',
                    'FixedAssets_Per_Employee',
                    'Working_Capital_to_Total_Assets',
                    'Quick_Assets/Total_Assets',
                    'Current_Assets/Total_Assets', 
                    'Cash/Total_Assets',
                    'Quick_Assets/Current_Liability',
                    'Cash/Current_Liability',
                    'Current_Liability_to_Assets']
for o in viz6:
    plt.figure()
    sns.histplot(x = o, data=bankrupt, bins=70)

---

Column 52-61 distribution plot

viz6 = ['Revenue_per_person',
                    'Operating_profit_per_person',
                    'FixedAssets_Per_Employee',
                    'Working_Capital_to_Total_Assets',
                    'Quick_Assets/Total_Assets',
                    'Current_Assets/Total_Assets', 
                    'Cash/Total_Assets',
                    'Quick_Assets/Current_Liability',
                    'Cash/Current_Liability',
                    'Current_Liability_to_Assets']

for o in viz6:
    plt.figure()
    sns.displot(x = o, data = bankrupt, kind = "ecdf", hue = "Bankrupt")

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column 52-61 correlation plot using heat map

relation = bankrupt.iloc[:,51:61]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 62-71 histogram

viz7 = ['Operating_Funds_to_Liability', 
                    'Inventory/Working_Capital', 
                    'Inventory/Current_Liability',
                    'Current_Liabilities/Liability', 
                    'Working_Capital/Equity', 
                    'Current_Liabilities/Equity',
                    'Long-term_Liability_to_CurrentAssets',
                    'Retained_Earnings_to_Total_Assets',
                    'Total_income/Total_expense',
                    'Total_expense/Assets']

for p in viz7:
    plt.figure()
    sns.histplot(x = p, data = bankrupt, bins=70)

---

Column 62-71 distribution plot

viz7 = ['Operating_Funds_to_Liability', 
                    'Inventory/Working_Capital', 
                    'Inventory/Current_Liability',
                    'Current_Liabilities/Liability', 
                    'Working_Capital/Equity', 
                    'Current_Liabilities/Equity',
                    'Long-term_Liability_to_CurrentAssets',
                    'Retained_Earnings_to_Total_Assets',
                    'Total_income/Total_expense',
                    'Total_expense/Assets']

for p in viz7:
    plt.figure()
    sns.displot(x = p, data = bankrupt, kind="ecdf", hue = 'Bankrupt')

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Columns 61 - 71 correlation plot using heat map

relation = bankrupt.iloc[:,61:71]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 72-81 histogram

viz8 = ['CurrentAsset_TurnoverRate',
                    'QuickAsset_TurnoverRate',
                    'Workingcapitcal_TurnoverRate',
                    'Cash_Turnover_Rate', 
                    'Cash_Flow_to_Sales',
                    'Fixed_Assets_to_Assets',
                    'Current_Liability_to_Liability',
                    'Current_Liability_to_Equity', 
                    'Equity_to_Long-term_Liability',
                    'Cash_Flow_to_Total_Assets']

for q in viz8:
    plt.figure()
    sns.histplot(x=q, data=bankrupt, bins=70)

---

Column 72-81 distribution plot

viz8 = ['CurrentAsset_TurnoverRate',
                    'QuickAsset_TurnoverRate',
                    'Workingcapitcal_TurnoverRate',
                    'Cash_Turnover_Rate', 
                    'Cash_Flow_to_Sales',
                    'Fixed_Assets_to_Assets',
                    'Current_Liability_to_Liability',
                    'Current_Liability_to_Equity', 
                    'Equity_to_Long-term_Liability',
                    'Cash_Flow_to_Total_Assets']

for q in viz8:
    plt.figure()
    sns.displot(x=q, data=bankrupt, kind="ecdf", hue='Bankrupt')

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Column 71-81 correlation plot using heat map

relation = bankrupt.iloc[:,71:81]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 82-91 histogram

viz9 = ['Cash_Flow_to_Liability', 
                    'CFO_to_Assets', 
                    'Cash_Flow_to_Equity',
                    'Current_Liability_to_current_Asset',
                    'Liability-Assets_Flag',
                    'Net_Income_to_Total_asset',
                    'Total_assets_to_GNP_prine',
                    'No-credit_Interval',
                    'Gross_Profit_to_Sales',
                    'Net_Income_to_Stockholders_Equity']

for r in viz9:
    plt.figure()
    sns.histplot( x=r, data=bankrupt, bins=70)

---

Column 82-91 distribution plot

viz9 = ['Cash_Flow_to_Liability', 
                    'CFO_to_Assets', 
                    'Cash_Flow_to_Equity',
                    'Current_Liability_to_current_Asset',
                    'Liability-Assets_Flag',
                    'Net_Income_to_Total_asset',
                    'Total_assets_to_GNP_prine',
                    'No-credit_Interval',
                    'Gross_Profit_to_Sales',
                    'Net_Income_to_Stockholders_Equity']

for r in viz9:
    plt.figure()
    sns.displot(x=r, data=bankrupt, kind="ecdf", hue = "Bankrupt")

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Column 81-91 correlation plot using heatmap

relation = bankrupt.iloc[:,81:91]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

Column 92-96 histogram

viz10 = ['Liability_to_equity',
                    'Degree_of_Financial_Leverage',
                    'Interest_Coverage_Ratio',
                    'Net_Income_Flag',
                    'Equity_to_Liability']

for s in viz10:
    plt.figure()
    sns.histplot(x=s, data=bankrupt, bins=70)

---

Column 92-96 distribution plot

viz10 = ['Liability_to_equity',
                    'Degree_of_Financial_Leverage',
                    'Interest_Coverage_Ratio',
                    'Net_Income_Flag',
                    'Equity_to_Liability']

for s in viz10:
    plt.figure()
    sns.displot(x=s, data=bankrupt, kind="ecdf", hue="Bankrupt")

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Column 91-96 correlation plot using heat map

relation = bankrupt.iloc[:,91:96]
c_plot = relation.corr()
plt.figure(figsize = (20,10))
sns.heatmap(c_plot, annot = True)

<AxesSubplot:>

---

After looking at all the visualizations and correlation plots we can conclue that the data points are either extremely correlated with some of the components or show no correlation with any of the components.

So, we need to use SMOTE analysis to balance the data.

SMOTE is performed on training data so we will split the data into test and training set first and then using the smote training data we will perform the models and further analysis.

However, one point to be noted is LDA and QDA analysis is done without SMOTE just to see how the variables are related / fit to the depedant variable despite the variation in the dataset.

from collections import Counter
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import Pipeline
from matplotlib import pyplot
from numpy import where

from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state = 33)

#Y = bankrupt[["Bankrupt"]]
Y = bankrupt.iloc[:, 0]
X = bankrupt.iloc[:, 1:95]

x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size=0.3, random_state=33)

newX_train, newY_train = sm.fit_sample(x_train,y_train.values.ravel())

pd.Series(newY_train).value_counts().plot.bar()

<AxesSubplot:>

x_test.shape

(2046, 94)

---

Linear and multivariate discriminant analysis

Iam converting all the negative variables her into positive variables for chi-square feature selection it takes positive variables only

X.apply(abs)

	Return_on_total_assetC	Return_on_total_assetA	Return_on_total_assetB	GrossProfit/NetSales	Realized_GrossProfit/NetSales	Operating_Income/NetSales	Pre-TaxIncome/NetSales	NetIncome/NetSales	Net_Non-operating_Income_Ratio	Net_Income-Exclude_DisposalGain/Loss/Net_Sales	...	Liability-Assets_Flag	Net_Income_to_Total_asset	Total_assets_to_GNP_prine	No-credit_Interval	Gross_Profit_to_Sales	Net_Income_to_Stockholders_Equity	Liability_to_equity	Degree_of_Financial_Leverage	Interest_Coverage_Ratio	Net_Income_Flag
0	0.370594	0.424389	0.405750	0.601457	0.601457	0.998969	0.796887	0.808809	0.302646	0.780985	...	0	0.716845	0.009219	0.622879	0.601453	0.827890	0.290202	0.026601	0.564050	1
1	0.464291	0.538214	0.516730	0.610235	0.610235	0.998946	0.797380	0.809301	0.303556	0.781506	...	0	0.795297	0.008323	0.623652	0.610237	0.839969	0.283846	0.264577	0.570175	1
2	0.426071	0.499019	0.472295	0.601450	0.601364	0.998857	0.796403	0.808388	0.302035	0.780284	...	0	0.774670	0.040003	0.623841	0.601449	0.836774	0.290189	0.026555	0.563706	1
3	0.399844	0.451265	0.457733	0.583541	0.583541	0.998700	0.796967	0.808966	0.303350	0.781241	...	0	0.739555	0.003252	0.622929	0.583538	0.834697	0.281721	0.026697	0.564663	1
4	0.465022	0.538432	0.522298	0.598783	0.598783	0.998973	0.797366	0.809304	0.303475	0.781550	...	0	0.795016	0.003878	0.623521	0.598782	0.839973	0.278514	0.024752	0.575617	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6814	0.493687	0.539468	0.543230	0.604455	0.604462	0.998992	0.797409	0.809331	0.303510	0.781588	...	0	0.799927	0.000466	0.623620	0.604455	0.840359	0.279606	0.027064	0.566193	1
6815	0.475162	0.538269	0.524172	0.598308	0.598308	0.998992	0.797414	0.809327	0.303520	0.781586	...	0	0.799748	0.001959	0.623931	0.598306	0.840306	0.278132	0.027009	0.566018	1
6816	0.472725	0.533744	0.520638	0.610444	0.610213	0.998984	0.797401	0.809317	0.303512	0.781546	...	0	0.797778	0.002840	0.624156	0.610441	0.840138	0.275789	0.026791	0.565158	1
6817	0.506264	0.559911	0.554045	0.607850	0.607850	0.999074	0.797500	0.809399	0.303498	0.781663	...	0	0.811808	0.002837	0.623957	0.607846	0.841084	0.277547	0.026822	0.565302	1
6818	0.493053	0.570105	0.549548	0.627409	0.627409	0.998080	0.801987	0.813800	0.313415	0.786079	...	0	0.815956	0.000707	0.626680	0.627408	0.841019	0.275114	0.026793	0.565167	1

6819 rows × 94 columns

# defining the LDA model and importing the library
from numpy import mean
from numpy import std
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.datasets import make_classification 
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold

# declaring model
LDA = LinearDiscriminantAnalysis()

# Define model evaluation method
cv = RepeatedStratifiedKFold(n_splits = 10, n_repeats=3, random_state=33)

# evaluate model
scores = cross_val_score(LDA, X, Y, scoring='accuracy', cv=cv, n_jobs=-1)

# accuracy
print('Accuracy: %3.f(%.3f)' % (mean(scores), std(scores)))

'''
# fiting LDA model
LDA.fit(newX,newY)

# making prediction 
yhat = LDA.predict([newX])

# summarising prediction
print("Predicted class: %d" % yhat)
'''

Accuracy:   1(0.005)

'\n# fiting LDA model\nLDA.fit(newX,newY)\n\n# making prediction \nyhat = LDA.predict([newX])\n\n# summarising prediction\nprint("Predicted class: %d" % yhat)\n'

---

Tuning LDA Huperparameter

from sklearn.model_selection import GridSearchCV

LDA = LinearDiscriminantAnalysis()
cv = RepeatedStratifiedKFold(n_splits = 10, n_repeats = 3, random_state = 33)
grid = dict()
grid['solver'] = ['svd','lsqr','eigen']

search = GridSearchCV(LDA, grid, scoring='accuracy', cv=cv, n_jobs = -1)

results = search.fit(X,Y)

print('Mean Accuracy: %.3f' % results.best_score_)
print('config: %s' % results.best_params_)

Mean Accuracy: 0.963
config: {'solver': 'svd'}

# Tuning the hyper parameters for using shrinkage hyper parameter 
from numpy import arange

LDA = LinearDiscriminantAnalysis(solver = 'lsqr')
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=33)

grid = dict()
grid['shrinkage'] = arange(0, 1, 0.01)

search = GridSearchCV(LDA, grid, scoring='accuracy', cv=cv, n_jobs = -1)

results = search.fit(X,Y)

print('Mean Accuracy: %.3f' % results.best_score_)
print('config: %s' % results.best_params_)

Mean Accuracy: 0.966
config: {'shrinkage': 0.45}

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

model = QuadraticDiscriminantAnalysis()

#Result = model.fit(newX,newY)
cv = RepeatedStratifiedKFold(n_splits = 20, n_repeats=10, random_state=33)

# evaluate model
scores = cross_val_score(LDA, newX, newY, scoring='accuracy', cv=cv, n_jobs=-1)
get_params = ([newX])
print("Accuracy scores", mean(scores), std(scores))

Accuracy scores 0.962824348801104 0.004189336978568809

---

---

Decision Tree classifier

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn import decomposition
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from numpy import mean
from numpy import std
from sklearn import preprocessing 
import matplotlib.pyplot as plt
from sklearn import feature_selection
from sklearn import tree

# This is just the highlight of the SMOTE that was performed earlier in order to be clear about the training and testing variables

from sklearn.model_selection import train_test_split
# Grnerating Test and Training Data
x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size=0.3, random_state=33)

newX_train, newY_train = sm.fit_sample(x_train,y_train.values.ravel())

# Training data Shape
print("X and Y training data shape {} {}".format(newX_train.shape,newY_train.shape))

X and Y training data shape (9214, 94) (9214,)

# Test data shape
print("X and Y testing data shape {} {}".format(x_test.shape,y_test.shape))

X and Y testing data shape (2046, 94) (2046,)

Feature selection

from sklearn import feature_selection

fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=10)
x_train_fs = fs.fit_transform(newX_train, newY_train)

np.set_printoptions(suppress=True, precision=2, linewidth=120)
print(list(X.columns))
print(fs.get_support())
print(fs.scores_)

['Return_on_total_assetC', 'Return_on_total_assetA', 'Return_on_total_assetB', 'GrossProfit/NetSales', 'Realized_GrossProfit/NetSales', 'Operating_Income/NetSales', 'Pre-TaxIncome/NetSales', 'NetIncome/NetSales', 'Net_Non-operating_Income_Ratio', 'Net_Income-Exclude_DisposalGain/Loss/Net_Sales', 'Operating_Expenses/Net Sales', 'Research_and_DevelopmentExpenses/Net Sales', 'CashFlow_from_Operating/Current Liabilities', 'Interest-bearing Debt/Equity', 'Effective Tax Rate', 'Net_Val_Per_ShareB', 'Net_Val_per_ShareA', 'Net_Val_Per_ShareC', 'EPS-Net_Income', 'Cash_Flow_Per_Share', 'Revenue_Per_Share(Yuan¥)', 'Operating_Income_Per_Share(Yuan¥)', 'Pretax_Income_Per_Share(Yuan¥)', 'Realized_Sales_Gross_Profit_Growth_Rate', 'Operating_Income_Growth', 'After_Tax_Net_Income_Growth', 'Regular_Net_Profit_Growth_Rate', 'Continuous_Net_Profit_Growth_Rate', 'Total_Asset_Growth', 'Total_Equity_Growth', 'Return_on_Total_Asset_Growth', 'Cash_Reinvestment_Ratio', 'Current_Ratio', 'Quick_Ratio', 'Interest_Expense_Ratio', 'Total_Liability/Equity_Ratio', 'Debt_ratio%', 'Networth/Assets', 'Long-term_fund_suitability_ratio(A)', 'Borrowing_dependency', 'Contingent_Liability/Equity', 'Operating_Income/Capital', 'Pretax_Income/Capital', '(Inventory+Accounts_Receivables)/Equity', 'Total_Asset_Turnover', 'Accounts_Receivable_Turnover', 'Average_Collection_Days', 'Inventory_Turnover_Rate(times)', 'Fixed_Assets_Turnover_Frequency', 'Equity_Turnover', 'Revenue_per_person', 'Operating_profit_per_person', 'FixedAssets_Per_Employee', 'Working_Capital_to_Total_Assets', 'Quick_Assets/Total_Assets', 'Current_Assets/Total_Assets', 'Cash/Total_Assets', 'Quick_Assets/Current_Liability', 'Cash/Current_Liability', 'Current_Liability_to_Assets', 'Operating_Funds_to_Liability', 'Inventory/Working_Capital', 'Inventory/Current_Liability', 'Current_Liabilities/Liability', 'Working_Capital/Equity', 'Current_Liabilities/Equity', 'Long-term_Liability_to_CurrentAssets', 'Retained_Earnings_to_Total_Assets', 'Total_income/Total_expense', 'Total_expense/Assets', 'CurrentAsset_TurnoverRate', 'QuickAsset_TurnoverRate', 'Workingcapitcal_TurnoverRate', 'Cash_Turnover_Rate', 'Cash_Flow_to_Sales', 'Fixed_Assets_to_Assets', 'Current_Liability_to_Liability', 'Current_Liability_to_Equity', 'Equity_to_Long-term_Liability', 'Cash_Flow_to_Total_Assets', 'Cash_Flow_to_Liability', 'CFO_to_Assets', 'Cash_Flow_to_Equity', 'Current_Liability_to_current_Asset', 'Liability-Assets_Flag', 'Net_Income_to_Total_asset', 'Total_assets_to_GNP_prine', 'No-credit_Interval', 'Gross_Profit_to_Sales', 'Net_Income_to_Stockholders_Equity', 'Liability_to_equity', 'Degree_of_Financial_Leverage', 'Interest_Coverage_Ratio', 'Net_Income_Flag']
[False False False False False False False False False False False  True False False False False False False False
 False False False False False False False False False  True False False False False False False False False False
 False False False False False False False False False False  True False False False False False False False False
 False  True False False False  True False False False  True False False False False  True False  True False  True
 False False False False False False False False False False  True False False False False False False False]
[3.62e+01 4.45e+01 3.68e+01 3.91e-01 3.87e-01 2.14e-05 3.33e-04 3.42e-04 4.79e-03 2.49e-04 2.66e+10 4.96e+11 3.08e-01
 4.83e+10 2.65e+02 1.19e+01 1.21e+01 1.20e+01 1.91e+01 5.42e-01 7.55e+09 1.22e+01 2.01e+01 3.00e-04 4.77e-03 3.53e-02
 3.57e-02 3.12e-03 1.11e+11 9.25e-02 6.26e-03 2.54e-01 1.39e+01 4.44e+10 6.50e-04 3.16e+10 8.55e+01 1.49e+01 7.37e-01
 1.94e+00 7.26e+00 1.19e+01 1.85e+01 2.49e-01 3.01e+01 2.47e+10 3.36e+10 3.92e+10 1.91e+12 6.61e-01 8.07e+10 1.82e+00
 1.05e+09 1.18e+01 5.72e+01 9.70e+00 1.77e+02 1.70e+10 5.08e+11 6.39e+01 2.01e+00 1.28e-04 1.82e+11 2.50e-01 3.04e-01
 1.12e+00 8.62e+10 2.16e+00 1.20e-01 2.06e+01 1.96e+10 2.19e+11 7.74e-05 3.69e+11 5.18e-06 1.06e+11 2.50e-01 1.12e+00
 5.39e+00 1.55e+00 3.20e-01 7.54e+00 1.06e-01 3.93e+01 1.00e+00 1.33e+01 1.08e+11 1.23e-03 3.91e-01 6.76e-01 1.80e+00
 1.95e-01 1.29e-07 0.00e+00]

print(X.columns[fs.get_support()].values)

['Research_and_DevelopmentExpenses/Net Sales' 'Total_Asset_Growth' 'Fixed_Assets_Turnover_Frequency'
 'Cash/Current_Liability' 'Inventory/Current_Liability' 'Long-term_Liability_to_CurrentAssets'
 'QuickAsset_TurnoverRate' 'Cash_Turnover_Rate' 'Fixed_Assets_to_Assets' 'Total_assets_to_GNP_prine']

for i in range(len(X.columns.values)):
    if fs.get_support()[i]:
        print("%10s %3.2f" % (X.columns.values[i], fs.scores_[i]))

Research_and_DevelopmentExpenses/Net Sales 495689040535.19
Total_Asset_Growth 111295705995.58
Fixed_Assets_Turnover_Frequency 1909676168349.49
Cash/Current_Liability 508179720932.41
Inventory/Current_Liability 181987953557.58
Long-term_Liability_to_CurrentAssets 86162894967.42
QuickAsset_TurnoverRate 219303226939.27
Cash_Turnover_Rate 368630107487.13
Fixed_Assets_to_Assets 105502057522.28
Total_assets_to_GNP_prine 107742719530.95

from sklearn import metrics

def measure_performance(X, y, clf, show_accuracy=True, show_classification_report=True, show_confussion_matrix=True):
    y_pred = clf.predict(X)   
    if show_accuracy:
         print("Accuracy:{0:.3f}".format(metrics.accuracy_score(y, y_pred)),"\n")
    if show_classification_report:
        print("Classification report")
        print(metrics.classification_report(y, y_pred),"\n")
      
    if show_confussion_matrix:
        print("Confussion matrix")
        print(metrics.confusion_matrix(y, y_pred),"\n")

from sklearn import tree

dt = tree.DecisionTreeClassifier(criterion='entropy')
dt.fit(x_train_fs, newY_train)
x_test_fs = fs.transform(x_test)
measure_performance(x_test_fs, y_test, dt, show_confussion_matrix=True)

Accuracy:0.901 

Classification report
              precision    recall  f1-score   support

           0       0.98      0.92      0.95      1992
           1       0.10      0.35      0.16        54

    accuracy                           0.90      2046
   macro avg       0.54      0.63      0.55      2046
weighted avg       0.96      0.90      0.93      2046
 

Confussion matrix
[[1824  168]
 [  35   19]] 

using cross validation to select features

#Selecting best features by percentile using cross validation
from sklearn.model_selection import cross_val_score
dt = tree.DecisionTreeClassifier(criterion='entropy')

percentiles = range(1, 100, 5)
results = []
for i in range(1, 100, 5):
    fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=i)
    x_train_fs = fs.fit_transform(newX_train, newY_train)
    scores = cross_val_score(dt, x_train_fs, newY_train, cv=5)
    print("%2d  %0.4f" % (i, scores.mean()))
    results = np.append(results, scores.mean())

 1  0.7493
 6  0.9057
11  0.9324
16  0.9384
21  0.9445
26  0.9487
31  0.9585
36  0.9573
41  0.9570
46  0.9571
51  0.9589
56  0.9565
61  0.9559
66  0.9543
71  0.9529
76  0.9520
81  0.9551
86  0.9534
91  0.9542
96  0.9579

optimal_percentile_ind = np.where(results == results.max())[0][0]
print("Optimal percentile of features:{0}".format(percentiles[optimal_percentile_ind]), "\n")
optimal_num_features = int(percentiles[optimal_percentile_ind]*len(X.columns)/100)
print("Optimal number of features:{0}".format(optimal_num_features), "\n")

# Plot percentile of features VS. cross-validation scores
import pylab as pl
pl.figure()
pl.xlabel("Percentage of features selected")
pl.ylabel("Cross validation accuracy")
pl.plot(percentiles,results)

Optimal percentile of features:51 

Optimal number of features:47 

[<matplotlib.lines.Line2D at 0x26031e2efd0>]

Evaluating the best featurs on test set

fs = feature_selection.SelectKBest(feature_selection.chi2, optimal_num_features)
x_train_fs = fs.fit_transform(newX_train, newY_train)
dt = tree.DecisionTreeClassifier(criterion='entropy')
dt.fit(x_train_fs, newY_train)
x_test_fs = fs.transform(x_test)
measure_performance(x_test_fs, y_test, dt, show_confussion_matrix=False)

D:\MYSOFTWARE\Anaconda\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass k=47 as keyword args. From version 0.25 passing these as positional arguments will result in an error
  warnings.warn("Pass {} as keyword args. From version 0.25 "

Accuracy:0.923 

Classification report
              precision    recall  f1-score   support

           0       0.99      0.94      0.96      1992
           1       0.17      0.48      0.25        54

    accuracy                           0.92      2046
   macro avg       0.58      0.71      0.60      2046
weighted avg       0.96      0.92      0.94      2046
 

We will now change the criterian parameter to see how the model perform with different parameter

dt = tree.DecisionTreeClassifier(criterion='entropy')
scores = cross_val_score(dt, x_train_fs, newY_train, cv=5)
print("Entropy criterion accuracy on cv: {0:.3f}".format(scores.mean()))

dt = tree.DecisionTreeClassifier(criterion='gini')
scores = cross_val_score(dt, x_train_fs, newY_train, cv=5)
print("Gini criterion accuracy on cv: {0:.3f}".format(scores.mean()))

Entropy criterion accuracy on cv: 0.959
Gini criterion accuracy on cv: 0.956

dt = tree.DecisionTreeClassifier(criterion='entropy')
dt.fit(x_train_fs, newY_train)
x_test_fs = fs.transform(x_test)
measure_performance(x_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=True)

Accuracy:0.924 

Classification report
              precision    recall  f1-score   support

           0       0.99      0.94      0.96      1992
           1       0.17      0.50      0.26        54

    accuracy                           0.92      2046
   macro avg       0.58      0.72      0.61      2046
weighted avg       0.96      0.92      0.94      2046
 

dt = tree.DecisionTreeClassifier(criterion='gini')
dt.fit(x_train_fs, newY_train)
x_test_fs = fs.transform(x_test)
measure_performance(x_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=True)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-7-c068e7e83629> in <module>
      1 dt = tree.DecisionTreeClassifier(criterion='gini')
----> 2 dt.fit(x_train_fs, newY_train)
      3 x_test_fs = fs.transform(x_test)
      4 measure_performance(x_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=True)

NameError: name 'x_train_fs' is not defined

Evaluating the accuracy base on the depth of the tree

dt = tree.DecisionTreeClassifier(criterion='entropy')
dt.set_params(max_depth=3, min_samples_leaf=10)

dt.fit(newX_train, newY_train)
measure_performance(x_test, y_test, dt, show_confussion_matrix=False, show_classification_report=True)

Accuracy:0.865 

Classification report
              precision    recall  f1-score   support

           0       1.00      0.86      0.93      1992
           1       0.15      0.85      0.25        54

    accuracy                           0.86      2046
   macro avg       0.57      0.86      0.59      2046
weighted avg       0.97      0.86      0.91      2046
 

from sklearn.tree import export_graphviz
from six import StringIO
from IPython.display import Image 
import pydotplus

newX_train.to_numpy()

array([[0.53, 0.6 , 0.58, ..., 0.03, 0.57, 1.  ],
       [0.37, 0.5 , 0.41, ..., 0.03, 0.56, 1.  ],
       [0.62, 0.67, 0.64, ..., 0.03, 0.57, 1.  ],
       ...,
       [0.43, 0.49, 0.48, ..., 0.03, 0.56, 1.  ],
       [0.23, 0.24, 0.26, ..., 0.03, 0.57, 1.  ],
       [0.4 , 0.43, 0.44, ..., 0.03, 0.56, 1.  ]])

newY_train

array([0, 0, 0, ..., 1, 1, 1], dtype=int64)

from sklearn.preprocessing import MinMaxScaler

scale = MinMaxScaler()
scale.fit(newX_train)
newX_train = scale.transform(newX_train)
x_test = scale.transform(x_test)
newX_train

array([[0.53, 0.6 , 0.58, ..., 0.05, 0.71, 0.  ],
       [0.37, 0.5 , 0.41, ..., 0.05, 0.71, 0.  ],
       [0.62, 0.67, 0.64, ..., 0.05, 0.71, 0.  ],
       ...,
       [0.43, 0.49, 0.48, ..., 0.05, 0.71, 0.  ],
       [0.23, 0.24, 0.26, ..., 0.05, 0.71, 0.  ],
       [0.4 , 0.43, 0.44, ..., 0.05, 0.71, 0.  ]])

y_test

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

implementing svm

newX_train.shape

(9214, 94)

newY_train.shape

(9214,)

x_test.shape

(2046, 94)

y_test.shape

(2046,)

newX_train

array([[0.53, 0.6 , 0.58, ..., 0.05, 0.71, 0.  ],
       [0.37, 0.5 , 0.41, ..., 0.05, 0.71, 0.  ],
       [0.62, 0.67, 0.64, ..., 0.05, 0.71, 0.  ],
       ...,
       [0.43, 0.49, 0.48, ..., 0.05, 0.71, 0.  ],
       [0.23, 0.24, 0.26, ..., 0.05, 0.71, 0.  ],
       [0.4 , 0.43, 0.44, ..., 0.05, 0.71, 0.  ]])

from sklearn.decomposition import PCA
pca = PCA(n_components=20)
pca.fit(newX_train)
variance_by_component = pca.explained_variance_ratio_
variance_by_component

array([0.17, 0.15, 0.11, 0.09, 0.08, 0.07, 0.06, 0.06, 0.05, 0.04, 0.03, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
       0.  , 0.  ])

eigenvalue = [0.17,0.15,0.11,0.09,0.08,0.07,0.06,0.06,0.05,0.04,0.03,0.01,0.01,
             0.01,0.01,0.01,0.01,0.01,0.,0.]
components = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]

plt.plot(components,eigenvalue)
plt.xlabel('components')
plt.ylabel('eigenvale')
plt.show()

sum(pca.explained_variance_ratio_[:19])

0.9634674387764315

from sklearn.decomposition import PCA
pca = PCA(n_components=19)
pca.fit(newX_train)
newX_train_PCA =pca.transform(newX_train)
x_test_PCA = pca.transform(x_test)

newX_train_PCA[0]

array([-0.38,  0.01, -0.  , -0.29,  0.28,  0.49,  0.4 , -0.27,  0.4 ,  0.23,  0.16,  0.05,  0.01,  0.02, -0.18,  0.8 ,
        0.11,  0.01, -0.07])

from sklearn.svm import SVC
classifier = SVC(kernel='linear')
classifier.fit(newX_train_PCA, newY_train)

SVC(kernel='linear')

y_pred = classifier.predict(x_test_PCA)

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

[[1699  293]
 [   9   45]]
              precision    recall  f1-score   support

           0       0.99      0.85      0.92      1992
           1       0.13      0.83      0.23        54

    accuracy                           0.85      2046
   macro avg       0.56      0.84      0.57      2046
weighted avg       0.97      0.85      0.90      2046

from sklearn.svm import SVC
from sklearn import svm
classifier = SVC(kernel='rbf')
classifier.fit(newX_train_PCA, newY_train)

SVC()

y_pred = classifier.predict(x_test_PCA)

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

[[1875  117]
 [  29   25]]
              precision    recall  f1-score   support

           0       0.98      0.94      0.96      1992
           1       0.18      0.46      0.26        54

    accuracy                           0.93      2046
   macro avg       0.58      0.70      0.61      2046
weighted avg       0.96      0.93      0.94      2046

NO PCA

from sklearn.svm import SVC
classifier = SVC(kernel='linear')
classifier.fit(newX_train, newY_train)

SVC(kernel='linear')

y_pred = classifier.predict(x_test)

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

[[1723  269]
 [   8   46]]
              precision    recall  f1-score   support

           0       1.00      0.86      0.93      1992
           1       0.15      0.85      0.25        54

    accuracy                           0.86      2046
   macro avg       0.57      0.86      0.59      2046
weighted avg       0.97      0.86      0.91      2046

from sklearn.svm import SVC
from sklearn import svm
classifier = SVC(kernel='rbf')
classifier.fit(newX_train, newY_train)

SVC()

y_pred = classifier.predict(x_test)

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

[[1758  234]
 [  16   38]]
              precision    recall  f1-score   support

           0       0.99      0.88      0.93      1992
           1       0.14      0.70      0.23        54

    accuracy                           0.88      2046
   macro avg       0.57      0.79      0.58      2046
weighted avg       0.97      0.88      0.92      2046